'''
@author: zhangkai
@license: (C) Copyright 2017-2023
@contact: jeffcobile@gmail.com
@Software : PyCharm
@file: unet.py
@time: 2020-06-07 15:39:19
@desc: 
'''
import torch
from ELib.model.segbase import SegBaseModel
from ELib.model.model_zoo import MODEL_ZOO


@MODEL_ZOO.register()
class UNet(SegBaseModel):
    def __init__(self, cfg):
        super(UNet, self).__init__(cfg, need_backbone=False)
        self.inc = DoubleConv(3, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        self.down4 = Down(512, 512)
        self.head = _UNetHead(self.nclass)

        self.__setattr__('decoder', ['head', 'auxlayer'] if self.aux else ['head'])

    def forward(self, x): # bs, 3, 480, 480
        size = x.size()[2:]
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)

        '''
        x1 : bs, 64, 480, 480
        x2 : bs,128, 240, 240
        x3 : bs,256, 120, 120
        x4 : bs,512,  60,  60
        x5 : bs,512,  30,  30
        '''

        outputs = list()
        x = self.head(x1, x2, x3, x4, x5) # bs, num_class, 480, 480
        x = torch.nn.functional.interpolate(x, size, mode='bilinear', align_corners=True)

        outputs.append(x)
        return tuple(outputs)


class _UNetHead(torch.nn.Module):
    def __init__(self, nclass, norm_layer=torch.nn.BatchNorm2d):
        super(_UNetHead, self).__init__()
        bilinear = True
        self.up1 = Up(1024, 256, bilinear)
        self.up2 = Up(512, 128, bilinear)
        self.up3 = Up(256, 64, bilinear)
        self.up4 = Up(128, 64, bilinear)
        self.outc = OutConv(64, nclass)

    def forward(self, x1, x2, x3, x4, x5):
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)

        logits = self.outc(x)
        return logits


class DoubleConv(torch.nn.Module):
    """(convolution => [BN] => ReLU) * 2"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.double_conv = torch.nn.Sequential(
            torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            torch.nn.BatchNorm2d(out_channels),
            torch.nn.ReLU(inplace=True),
            torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            torch.nn.BatchNorm2d(out_channels),
            torch.nn.ReLU(inplace=True)
        )

    def forward(self, x):
        return self.double_conv(x)


class Down(torch.nn.Module):
    """Downscaling with maxpool then double conv"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.maxpool_conv = torch.nn.Sequential(
            torch.nn.MaxPool2d(2),
            DoubleConv(in_channels, out_channels)
        )

    def forward(self, x):
        return self.maxpool_conv(x)


class Up(torch.nn.Module):
    """Upscaling then double conv"""

    def __init__(self, in_channels, out_channels, bilinear=True):
        super().__init__()

        # if bilinear, use the normal convolutions to reduce the number of channels
        if bilinear:
            self.up = torch.nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        else:
            self.up = torch.nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2)

        self.conv = DoubleConv(in_channels, out_channels)

    def forward(self, x1, x2):
        x1 = self.up(x1)
        # input is CHW
        diffY = torch.tensor([x2.size()[2] - x1.size()[2]]) # width
        diffX = torch.tensor([x2.size()[3] - x1.size()[3]]) # height

        x1 = torch.nn.functional.pad(x1, [diffX // 2, diffX - diffX // 2,
                        diffY // 2, diffY - diffY // 2])
        # if you have padding issues, see
        # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
        # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)


class OutConv(torch.nn.Module):
    def __init__(self, in_channels, out_channels):
        super(OutConv, self).__init__()
        self.conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1)

    def forward(self, x):
        return self.conv(x)